Undergraduate students

COMP 498 Neuroimage Computing (3 credits)

Prerequisites: ENGR 371 or COMP233. This course covers concepts, theories and practical knowledge in brain image processing and analysis. A practical introduction of medical imaging principles and image reconstruction will be provided. Topics to be covered include brain atlasing, computational anatomy, radiomics, tractography, image segmentation/classification, deep learning in neuroimaging, image-guided neurosurgery, and computer-assisted diagnosis. State-of-the-art software for neuroimage processing and analysis, as well as popular open source databases will also be covered through assignments and a project. Lectures: 3 hours per week.

COMP 499 Topics in Computer Science with Lab: Deep Learning (4 credits)

Prerequisite: COMP 432 or permission of instructor. This course introduces conceptual and practical aspects of deep learning. Topics covered will include commonly used deep learning model architectures, loss functions, regularization, optimization methods, and software tools. We will study applications in supervised, unsupervised learning, and reinforcement learning. Finally, the course will also aim to familiarize the students with theoretical results and open questions in the approximation and generalization of deep learning models as well as empirical results about the emergent properties of deep networks. A final project will be required. Lectures: three hours per week. Laboratory: two hours per week.